Atomic Physicochemical Parameters for Three‐Dimensional Structure‐Directed Quantitative Structure‐Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity

Ghose, Arup K.; Crippen, Gordon M.

doi:10.1002/jcc.540070419

Cited by 600 publications

(419 citation statements)

References 9 publications

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“…18,19 Contrary to Byvatov et al 12 we solely base our study on a Ghose-Crippen parametrization of the chemicals. 13 However, before feeding it into any of the learning machines we preprocess the GC descriptors by adding one and then computing the log. This transformation helped to increase the numerical stability of large scale SVM optimization.…”

Section: Methodsmentioning

confidence: 99%

Classifying ‘Drug-likeness' with Kernel-Based Learning Methods

Müller

Rätsch

Sonnenburg

et al. 2005

J. Chem. Inf. Model.

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In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set of descriptors of the substance. We were able to drastically improve the recent result by Byvatov et al. (2003) on this task and achieved an error rate of about 7% on unseen compounds using Support Vector Machines. We see a very high potential of such machine learning techniques for a variety of computational chemistry problems that occur in the drug discovery and drug design process.

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Section: Methodsmentioning

confidence: 99%

Classifying ‘Drug-likeness' with Kernel-Based Learning Methods

Müller

Rätsch

Sonnenburg

et al. 2005

J. Chem. Inf. Model.

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“…In a previous paper (Lomba et al 2013;Lomba et al 2011) values of the logarithm of 1-octanol/water partition coefficient, log P, were computed in silico using the ALOGP method (Ghose and Crippen 1986;Ghose et al 1988;Viswanadhan et al 1989) with values of -0.14 for LA, -0.17 for ML, 0.38 for EL and 1.36 for BL. We have compared the computed log P values to those predicted with the KowWin method (available on http://esc.syrres.com/esc/est_kowdemo.htm) (Meylan and Howard 1995).…”

Section: Physico-chemical Properties Of Levulinates Related To Toxicitymentioning

confidence: 99%

Ecotoxicity studies of the levulinate ester series

et al. 2014

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Abstract:The increasing interest in the development of novel green solvents has led to the synthesis of benign alternative products with minimized environmental impacts.However, most of published studies on green solvents focus primarily on their physicochemical properties, with limited emphasis on absence of ecotoxicological assessment. In this study, we evaluated the acute ecotoxicity of four levulinates (levulinic acid, methyl levulinate, ethyl levulinate and butyl levulinate) on freshwater algae (Chlamydomonas reinhardtii), bacteria (Vibrio fischeri), daphnids (Daphnia magna) and earthworms (Eisenia foetida) using various dose-response tests. As a general trend, the toxicity of levulinate esters in aquatic exposure (assessed as the EC 50 ) increased as a function of increasing alkyl chain length; accordingly, the most toxic compound for the aquatic organisms was butyl levulinate, followed by ethyl levulinate and methyl levulinate. The most toxic compound for Eisenia foetida (terrestrial exposure) was methyl levulinate, followed by ethyl levulinate, butyl levulinate and levulinic acid; in this case, we observed an inverse relationship between toxicity and alkyl chain length. Based on both the lowest EC 50 found in the aquatic media and the ratio between predicted environmental concentration (PEC) and the predicted no-effect concentration (PNEC), we have estimated the maximum allowable values in the 2 environment for these chemicals to be 1.093 mg L -1 for levulinic acid, 2.761 mg L -1 for methyl levulinate, 0.982 mg L -1 for ethyl levulinate and 0.151 mg L -1 for butyl levulinate.

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“…This was done by calculating log P values for water/octanol partitioning using the Ghose-Crippen method. 44 Polarizability and dipole moment were also calculated for the methylated bases. Surface area was calculated for the free (unsubstituted) bases.…”

Section: Physical Properties Of the Aromatic Stacking Speciesmentioning

confidence: 99%

Factors Contributing to Aromatic Stacking in Water: Evaluation in the Context of DNA

et al. 2000

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We report the use of thermodynamic measurements in a self-complementary DNA duplex (5′-dXCGCGCG) 2 , where X is an unpaired natural or nonnatural deoxynucleoside, to study the forces that stabilize aqueous aromatic stacking in the context of DNA. Thermal denaturation experiments show that the core duplex (lacking X) is formed with a free energy (37 °C) of −8.1 kcal·mol −1 in a pH 7.0 buffer containing 1 M Na + . We studied the effects of adding single dangling nucleosides (X) where the aromatic "base" is adenine, guanine, thymine, cytosine, pyrrole, benzene, 4-methylindole, 5-nitroindole, trimethylbenzene, difluorotoluene, naphthalene, phenanthrene, and pyrene. Adding these dangling residues is found to stabilize the duplex by an additional −0.8 to −3.4 kcal·mol −1 . At 5 μM DNA concentration, T m values range from 41.7 °C (core sequence) to 64.1 °C (with dangling pyrene residues). For the four natural bases, the order of stacking ability is A > G ≥ T = C. The nonpolar analogues stack more strongly in general than the more polar natural bases. The stacking geometry was confirmed in two cases (X = adenine and pyrene) by 2-D NOESY experiments. Also studied is the effect of ethanol cosolvent on the stacking of natural bases and pyrene. Stacking abilities were compared to calculated values for hydrophobicity, dipole moment, polarizability, and surface area. In general, hydrophobic effects are found to be larger than other effects stabilizing stacking (electrostatic effects, dispersion forces); however, the natural DNA bases are found to be less dependent on hydrophobic effects than are the more nonpolar compounds. The results also point out strategies for the design nucleoside analogues that stack considerably more strongly than the natural bases; such compounds may be useful in stabilizing designed DNA structures and complexes.

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Atomic Physicochemical Parameters for Three‐Dimensional Structure‐Directed Quantitative Structure‐Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity

Cited by 600 publications

References 9 publications

Classifying ‘Drug-likeness' with Kernel-Based Learning Methods

Classifying ‘Drug-likeness' with Kernel-Based Learning Methods

Ecotoxicity studies of the levulinate ester series

Factors Contributing to Aromatic Stacking in Water: Evaluation in the Context of DNA

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